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### function input: G, C, z, N
### parameters estimates: ss.diag, ss.off, sss, mean.diag, mean.off, ss.bound
### function output : List of Cov Matrix 1,....,C.
Gcov_c = function(GG, C, z, N)
{
ss.diag = rep(1,C)
mean.diag = rep(1,C)
ss.off = ss.bound = nn = matrix(0, nrow = C, ncol = C)
mean.off = matrix(0, nrow = C, ncol = C)
sss = nnn = array(0,dim = c(C, C, C))
Cov = list()
# step one - get intiail gamma and labels
step_one = get_gamma_labels_c(z, C)
labels = c(step_one$labels)
gamma = lapply(step_one$gamma, c)
# step two
C = max(labels)
step_two = step_two_c(C, gamma, GG)
ss.off = step_two$ss_off
ss.diag = c(step_two$ss_diag)
mean.off = step_two$mean_off
mean.diag = c(step_two$mean_diag)
ss.off = ss.off + t(ss.off) - diag(diag(ss.off))
####### ss.diag and ss.off are completely cluster specific and symmetric
###Boundary Covariance Estimates: non-symmetric
###Total number of Boundary parameters: C
# step three
step_three = step_three_c(C, gamma, GG, mean.off, mean.diag)
ss.bound = step_three$ss_bound
#Offdiagonals Estimates: symmetric
## 1<=ii <=jj <= kk <= C
## Total number of parameters = C choose 3 + 2*C choose 2 + C choose 1.
## Hardest part.
step_four = step_four_c(C, gamma, GG, mean.off, mean.diag)
sss = step_four$sss
step_five = step_five_c(C, gamma, ss.off, ss.diag, labels, sss, ss.bound, N)
Cov = step_five$Cov
return(list(Cov = Cov, gamma = gamma))
}
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